AI System for Predicting Reading Time and Reading Complexity for Reviewing 2D/3D Breast Images

ABSTRACT

Examples of the present disclosure describe systems and methods for predicting the reading time and/or reading complexity of a breast image. In aspects, a first set of data relating to the reading time of breast images may be collected from one or more data sources, such as image acquisition workstations, image review workstations, and healthcare professional profile data. The first set of data may be used to train a predictive model to predict/estimate an expected reading time and/or an expected reading complexity for various breast images. Subsequently, a second set of data comprising at least one breast image may be provided as input to the trained predictive model. The trained predictive model may output an estimated reading time and/or reading complexity for the breast image. The output of the trained predictive model may be used to prioritize mammographic studies or optimize the utilization of available time for radiologists.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of application Ser. No. 17/033,372,filed Sep. 25, 2020, which claims priority to provisional applicationSer. No. 62/907,257, filed Sep. 27, 2019, entitled “A1 SYSTEM FORPREDICTING READING TIME AND READING COMPLEXITY FOR REVIEWING 2D/3DBREAST IMAGES,” which applications are incorporated herein by referencein their entirety.

BACKGROUND

Modern breast care involves extensive analysis of radiological images.Given the recent advances in radiological imaging, the amount of dataradiologists are required to parse through and evaluate is increasingexponentially. This exponential increase in data often causes a largevariation in the time to read radiological images. This variability intime is further exacerbated by individual circumstances of theradiologists (e.g., years of experience, areas of expertise, availableimage reading tools, etc.) as well as the specific contents of the datato be reviewed. As a result, optimally distributing workload (e.g.,radiological images) to available radiologists in a screening center hasproven challenging.

It is with respect to these and other general considerations that theaspects disclosed herein have been made. Also, although relativelyspecific problems may be discussed, it should be understood that theexamples should not be limited to solving the specific problemsidentified in the background or elsewhere in this disclosure.

SUMMARY

Examples of the present disclosure describe systems and methods forpredicting reading time and/or complexity of a mammographic exam. Inaspects, a first set of data relating to the reading time ofmammographic exams may be collected from one or more data sourcesassociated with a healthcare professional, such as image acquisitionworkstations, image review workstations, healthcare professional profiledata, and preexisting patient data. The first set of data may be used totrain a predictive model to predict/estimate an expected reading timefor various breast images. Subsequently, a second set of data comprisingat least one breast image may be provided as input to the trainedpredictive model. The trained predictive model may output an estimatedreading time and/or reading complexity for the breast image. Theestimated reading time may be used to prioritize mammographic exams oroptimize the utilization of available time for clinical professionals.

Aspects of the present disclosure provide a system comprising: at leastone processor; and memory coupled to the at least one processor, thememory comprising computer executable instructions that, when executedby the at least one processor, performs a method comprising: collectinga first set of data, wherein the first set of data comprises: breastimage data, user profile data for a reader of the breast image data, andevaluation data for the breast image data; using the first set of datato train a predictive model to predict an expected reading time for thebreast image data; collecting a second set of data, wherein the secondset of data comprises at least the breast image; applying the second setof data to the trained predictive model; and receiving, from the trainedpredictive model, an estimated reading time for the breast image.

Aspects of the present disclosure further provide a method comprising:collecting a first set of data, wherein the first set of data comprises:breast image data, user profile data for a reader of the breast imagedata, and evaluation data for the breast image data; using the first setof data to train a predictive model to predict an expected reading timefor the breast image data; collecting a second set of data, wherein thesecond set of data comprises at least the breast image; applying thesecond set of data to the trained predictive model; and receiving, fromthe trained predictive model, an estimated reading time for the breastimage.

Aspects of the present disclosure further provide a computer-readablemedia storing computer executable instructions that when executed causea computing system to perform a method comprising: collecting a firstset of data, wherein the first set of data comprises: breast image data,user profile data for a reader of the breast image data, and evaluationdata for the breast image data; using the first set of data to train apredictive model to predict an expected reading time for the breastimage data; collecting a second set of data, wherein the second set ofdata comprises at least the breast image; applying the second set ofdata to the trained predictive model; and receiving, from the trainedpredictive model, an estimated reading time for the breast image.

Aspects of the present disclosure provide a system comprising: at leastone processor; and memory coupled to the at least one processor, thememory comprising computer executable instructions that, when executedby the at least one processor, performs a method comprising: collectinga set of data, wherein the set of data comprises at least mammographyexam data; applying the set of data to a predictive model trained topredict an expected reading time for the mammography exam data; andreceiving, from the predictive model, an estimated reading time for themammography exam data.

Aspects of the present disclosure provide a system comprising: at leastone processor; and memory coupled to the at least one processor, thememory comprising computer executable instructions that, when executedby the at least one processor, performs a method comprising: collectinga first set of data, wherein the first set of data comprises: firstmammographic exam data for one or more patients; user profile data forone or more mammographic exam readers of the first mammographic examdata; and evaluation data for the one or more mammographic exam readers;updating a case complexity index based on the first set of data, whereinthe case complexity index comprises mappings between complexity valuesand factors affecting an amount of time required to interpret secondmammographic exam data; collecting a second set of data, wherein thesecond set of data comprises the second mammographic exam data;providing the case complexity index and the second set of data to apredictive model, wherein the predictive model is configured todetermine a complexity for the second mammographic exam data based onthe case complexity index; receiving, from the trained predictive model,an estimated complexity for reading the second mammographic exam data;and displaying the estimated complexity.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter. Additionalaspects, features, and/or advantages of examples will be set forth inpart in the description which follows and, in part, will be apparentfrom the description, or may be learned by practice of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive examples are described with reference tothe following figures.

FIG. 1 illustrates an overview of an example system for predictingreading time and/or reading complexity of a mammographic exam, asdescribed herein.

FIG. 2 illustrates an overview of an example input processing system forpredicting reading time and/or reading complexity of a mammographicexam, as described herein.

FIG. 3 illustrates an example method for predicting reading time of amammographic exam, as described herein.

FIG. 4 illustrates an example method for predicting reading complexityand/or reading time of a mammographic exam, as described herein.

FIG. 5A illustrates an example user interface that is associated withthe automated clinical workflow decisions described herein.

FIG. 5B illustrates an analytics dialog interface associated with theexample user interface of FIG. 5A.

FIG. 6 illustrates one example of a suitable operating environment inwhich one or more of the present embodiments may be implemented.

DETAILED DESCRIPTION

Medical imaging has become a widely used tool for identifying anddiagnosing abnormalities, such as cancers or other conditions, withinthe human body. Medical imaging processes such as mammography andtomosynthesis are particularly useful tools for imaging breasts toscreen for, or diagnose, cancer or other lesions with the breasts.Tomosynthesis systems are mammography systems that allow high resolutionbreast imaging based on limited angle tomosynthesis. Tomosynthesis,generally, produces a plurality of X-ray images, each of discrete layersor slices of the breast, through the entire thickness thereof. Incontrast to conventional two-dimensional (2D) mammography systems, atomosynthesis system acquires a series of X-ray projection images, eachprojection image obtained at a different angular displacement as theX-ray source moves along a path, such as a circular arc, over thebreast. In contrast to conventional computed tomography (CT),tomosynthesis is typically based on projection images obtained atlimited angular displacements of the X-ray source around the breast.Tomosynthesis reduces or eliminates the problems caused by tissueoverlap and structure noise present in 2D mammography imaging.

In modern breast care centers, the images produced using medical imagingare evaluated by healthcare professionals to determine the optimalbreast care path for patients. Due to advances in medical imaging(especially radiological imaging), the accuracy and granularity ofinformation in produced images continue to increase. For example, alarger number of computer-aided detection (CAD) marks identifying imagefeatures may be added to an image. Although useful for analyticalaccuracy, the increased number of identified image features may increasethe difficult of reading the image, which increases the time necessaryto read the image. Additionally, the advances in medical imaging haveresulted in an exponential increase in the volume of data healthcareprofessionals must review. The coupling of increased image readingcomplexity and the exponential increase in available data causes a largevariation in medical image reading times, which depend upon breasttypes, the presence of disease and abnormalities, breast density, theimage reader's experience, the type of mammographic exam performed, etc.Due to the large variation in medical image reading times, it is oftendifficult for screening centers to distribute workloads optimally toavailable radiologists. As a result, many screening centers experiencedecreased productivity and increased costs.

To address such issues with suboptimal workload distributions, thepresent disclosure describes systems and methods for predicting readingtime and/or reading complexity of a mammographic exam. In aspects, afirst set of mammographic exam data relating to one or more 2D and/or 3Dbreast images and information relating to the readers (human orelectronic) of the breast images may be collected from various datasources. Mammographic exam data, as used herein, may refer toinformation relating to breast image data (e.g., pixel image data andimage header data), evaluation data for the breast image data (e.g.,study open and close times, reader workload, and reading tools used),user profile-related data for a reader/evaluator of the breast imagedata (e.g., reader experience, reader expertise, etc.), preexistingpatient data (e.g., patient history records/reports and previouslycollected patient image data), reader opinion data for the breast imagedata (e.g., reader estimations of reading times or reading complexity),biopsy data, annotations and/or labeled data, and the like. A reader, asused herein, may refer to medical or clinical professional who istrained to review a mammographic exam. Examples of data sources include,but are not limited to, image acquisition workstations, image reviewworkstations, hospital information systems (HISs), patient recordsystems, reader profile systems, training data repositories, andtest/training systems. The first set of mammographic exam data, whichmay include labeled and/or unlabeled training data, may be used as inputto train one or more artificial intelligence (AI) models. A model, asused herein, may refer to a predictive or statistical utility or programthat may be used to determine a probability distribution over one ormore character sequences, classes, objects, result sets or events,and/or to predict a response value from one or more predictors. A modelmay be based on, or incorporate, one or more rule sets, machine learning(ML), a neural network, or the like.

In aspects, a second set of mammographic exam data may be collected fromone or more of the various data sources described above and provided tothe trained AI model. The second set of mammographic exam data maycomprise data that is similar to, or the same as, the first set ofmammographic exam data. In some examples, however, the second set ofmammographic exam data may not include training data. Based on thesecond set of mammographic exam data, the trained AI model may produceone or more outputs. Example outputs include, but are not limited to,predicted/estimated reading times for one or more images in the secondset of mammographic exam data, a complexity rating for reading an imagein the second set of mammographic exam data, and time slotavailabilities and/or assignments for one or more radiologists. Thecomplexity rating may indicate the difficulty or complexity of reading amammographic exam or images thereof. The difficulty or complexity ofreading a mammographic exam may be based on factors, such breast type,breast density, number of CAD marks, etc. The complexity rating mayinfer or be correlated with a time for reading a mammographic exam. Forinstance, the complexity rating and the reading time for a mammographicexam may be related such that the reading time increases as thecomplexity rating increases. In some aspects, the one or more outputs ofthe trained AI model may be provided to one or more healthcareprofessionals and used to balance or optimize the workloads of availableradiologists. The balancing/optimization of the workloads may beperformed manually by a healthcare professional, or automatically by thetrained AI model.

Accordingly, the present disclosure provides a plurality of technicalbenefits including, but not limited to: generating automated estimatesof time required for reading a mammographic exam, generating automatedclassifications of mammographic exam complexity, leveraging image readerinformation (e.g., statistics, experience, and credentials) to estimatemedical image reading times, automating optimized workload distribution,automated scheduling of reading of mammographic exams, trainingpredictive models based on subjective reader profile factors and/orreader statistics, and training predictive models based on preexistingpatient data.

FIG. 1 illustrates an overview of an example system for predictingreading time and/or reading complexity of a mammographic exam asdescribed herein. Example system 100 as presented is a combination ofinterdependent components that interact to form an integrated system forpredicting reading time and/or complexity of a mammographic exam.Components of the system may be hardware components or softwarecomponents (e.g., applications, application programming interfaces(APIs), modules, virtual machines, or runtime libraries) implemented onand/or executed by hardware components of the system. System 100 mayprovide an operating environment for software components to executeaccording to operating constraints, resources, and facilities of system100. In one example, the operating environment and/or softwarecomponents may be provided by a single processing device, as depicted inFIG. 5 . In other examples, the operating environment and softwarecomponents of systems disclosed herein may be distributed acrossmultiple devices. For instance, input may be entered on a client deviceand information may be processed or accessed using other devices in anetwork, such as one or more server devices.

As one example, the system 100 may comprise computing devices 102A,102B, and 102C (collectively, “computing device(s) 102”), processingsystem 108, and network 106. One of skill in the art will appreciatethat the scale of systems such as system 100 may vary and may includemore or fewer components than those described in FIG. 1 . For instance,in some examples, the functionality and/or data provided by computingdevice(s) 102 may be integrated into a single computing device orsystem. Alternately, the functionality and/or data of processing systems106 and/or 108 may be distributed across multiple systems and devices.

Computing device(s) 102 may be configured to receive mammographic examdata relating to a healthcare patient and/or healthcare professional.The mammographic exam data may be received using one or more userinterfaces (e.g., a graphical user interface (“GUI”), command line, menudriven interface, or data feed) or sensors (e.g., microphones,touch-based sensors, keyboards, pointing/selection tools,optical/magnetic scanners, or accelerometers) of computing device(s)102. Examples of computing device(s) 102 include, but are not limitedto, image acquisition systems (e.g., X-ray, ultrasound, and magneticresonance imaging (MRI) systems), image review workstations, HISdevices, patient record devices, mobile healthcare devices (e.g.,wearable devices, mobile phones, and tablets), and devices storinghealthcare professional information. Computing device(s) 102 may storethe mammographic exam data locally on computing device(s) 102 and/orremotely in one or more data storage locations, such as data stores 104Aand 104B (collectively, “data store(s) 104”), via network 106. Computingdevice(s) 102 and data store(s) 104 may be located in one or morehealthcare facilities, in a facility associated with a healthcarefacility, or in the possession of a healthcare professional. Inexamples, the mammographic exam data may be provided to computingdevice(s) 102 using manual processes, automatic processes, or somecombination thereof. For instance, a healthcare professional located ata healthcare facility may manually enter mammographic exam data into oneor more computing devices. Alternately, device located remotely from thehealthcare facility may automatically upload mammographic exam data toone or more computing devices of the healthcare facility. As a specificexample, a computing device located at the residence of a healthcareprofessional may automatically upload mammographic exam data to ahealthcare facility device as part of a daily synchronization process.

Processing system 108 may be configured to train and/or provide a MLmodel. In aspects, processing system 108 may have access to one or moresources of mammographic exam data, such as computing device(s) 102and/or data source(s) 104, via network 106. A first set of mammographicexam data may be provided as input to processing system 108. Processingsystem 108 may use the first set of mammographic exam data to train oneor more AI processing components. For example, processing system 108 maytrain an artificial neural network, a support vector machine (SVM), alinear reinforcement model, a random decision forest, or a similar MLtechnique. After the AI processing component has been trained, a secondset of mammographic exam data may be provided as input to processingsystem 108. Based on the second set of mammographic exam data,processing system 108 may generate one or more outputs, such asestimated reading times for an image in the second set of mammographicexam data, a complexity rating or label for reading an image in thesecond set of mammographic exam data, one or more reader-specificestimated reading times, reading complexities, or reading labels, timeslot availabilities and/or assignments for one or more radiologists,etc. The outputs may be provided (or made accessible) to othercomponents of system 100, such as computing device(s) 102. In examples,the outputs may be evaluated by one or more healthcare professionals todetermine study prioritization and/or optimization of workloaddistribution.

FIG. 2 illustrates an overview of an example input processing system 200for predicting reading time and/or reading complexity of a mammographicexam, as described herein. The reading prediction techniques implementedby input processing system 200 may comprise the reading predictiontechniques and data described in the system of FIG. 1 . In someexamples, one or more components (or the functionality thereof) of inputprocessing system 200 may be distributed across multiple devices and/orsystems. In other examples, a single device may comprise the componentsof input processing system 200.

With respect to FIG. 2 , input processing system 200 may comprise datacollection engine 202, processing engine 204, and output engine 206.Data collection engine 202 may be configured to access and/or collect aset of data. In aspects, data collection engine 202 may have access toinformation relating to one or more 2D/3D breast images and/or relatingto the readers/evaluators of the mammographic exam. The information maycomprise or represent various types of data, such as text data, speechdata, image data, video data, gesture data, etc. At least a portion ofthe information may be test data or training data that includes labeleddata, images, and known mammographic exam reading times. In someexamples, the information may be stored in and/or collected from one ormore computing devices located in, or accessible to, a healthcarefacility or a healthcare professional. The information may includebreast image data, evaluation data for the breast image data, userprofile-related data for a reader/evaluator of the breast image data,and the like.

In other examples, the information may be collected from a user via aninterface, such as user interface 203. User interface 203 may beconfigured to collect user input from or relating to one or morehealthcare professionals. Examples of the user input includeidentification information, performance statistics, current and/orhistoric workload information, user estimations of reading times orreading complexities for images or image types, and other userprofile-related information. User interface 203 may comprise varioususer interface elements for collecting the user input and/or navigatingor interacting with results provided by user interface 203. As oneexample, user interface 203 may comprise at least an image data listcomponent, an image data review component, and a self-evaluationcomponent. The image data list component may be configured to display alist of one or more image data file names or identifiers. The image datareview component may be configured to provide a preview or view of theimage data files provided by the image data list component. Theself-evaluation component may be configured to present a set of optionsthat enable a user to provide an estimated reading time or readingcomplexity for image data files provided by the image data listcomponent. The estimated reading time may represent a reader's opinionof the amount of time required by the reader to read a selectedmammographic exam. The estimated reading complexity may represent areader's opinion of the reading complexity of a selected mammographicexam. The estimated reading complexity may be provided as a label (e.g.,easy, medium, hard) or a numerical value (e.g., on a scale of 0-100).Similarly, the estimated reading time may be provided for the reader toselect as a label (e.g., fast, medium, slow). Such subjectiveinformation, such as the reader's perception of the reading may be usedto train the AI processing algorithms or models as described below.Similarly, the subjective information may be used as scores or weightsin determining the reading time and/or complexity. For example, for lowdensity, fatty breasts without concerning findings which are lessstressful, the perception of reading time may be ‘fast’, although inreality a reader may have taken additional time to scroll through andassess the mammographic exam.

Processing engine 204 may be configured to process the receivedinformation. In aspects, the received information may be provided toprocessing engine 204. Processing engine 204 may apply one or more AIprocessing algorithms or models to the received information. Forexample, processing engine 204 may apply a supervised learning model forclassification and regression analysis of the received information. Insome aspects, the received information may be used by processing engine204 (or an alternate component of input processing system 200) to trainthe AI processing algorithms or models. The trained AI processingalgorithms or models may then be used to evaluate received informationin order to determine correlations between the received information andthe training data used to train the AI processing algorithms or models.The evaluation may include an analysis of various factors that mayinfluence the reading time of an image. Such factors include, but arenot limited to, the number of lesions (or abnormalities/concerningfindings) detected in the image, the type of anomalies/findingsidentified by the image, the location of evaluation within the breast(e.g., superior, medial inferior or lateral), the symmetry between apatient's breasts, the number of image slices generated, breast density,breast thickness, breast area, breast tissue composition structure,breast tissue patterns, number of computer-aided detection (CAD)markers, type of image processing used (e.g., artifact reduction,de-noising, etc.), breast positioning parameters of an imaged breast,noise and contrast parameters, type or study performed (e.g., screening,diagnostic, etc.), number of exposures, types of compression paddlesused, time of day of the reading, type of workstation and tools used forthe reading (e.g., ease of pan/zoom, availability of “smart” hangingprotocols, availability of easy annotation tools, availability oftracking, auto-mapping, and “smart” scrolling tools, etc.), reading toolusage (e.g., number of click events, scroll events, and zoom in/outevents), user focus data (e.g., dwell time, eye gaze, and hover events),reader experience, reader specialization and training, reader age,reader reading time/complexity opinions or estimates, and readerproficiency.

In some aspects, the various factors analyzed by processing engine 204may be manually selected or weighted according to one or more criteria,such as user preference. For example, user interface 203 may enableusers to select a set of factors to be considered when analyzingreceived information. The user interface may also enable users to assignimportance scores or weights to the set of factors or modify importancescores or weights previously assigned to the set of factors. Forexample, the self-evaluation component described above may enable a userto assign or modify scores or weights to the set of factors. Further,the self-evaluation component may enable the user to navigate orotherwise interact with set of factors. The scores or weights mayindicate the perceived/determined importance of certain factors withrespect to other factors. In other aspects, the various factors analyzedby processing engine 204 may be automatically selected or weighted usingone or more AI or ML techniques. As one specific example, readerinformation for one or more readers may be provided to processing engine204. The reader information may include at least reader experience,previous evaluation data for the reader (e.g., previous reading times),and reader opinions or estimates of reading time and/or readingcomplexity for one or more images or image types. Based on the providedreader information and/or previous evaluation results, processing engine204 may determine that the previous reading times and/or readerestimates of reading times for clinical professionals having moreexperience and more advanced sensitivity and specificity in detectingmalignant findings should be given more weight than the same information(e.g., previous reading times and/or reader estimates of reading times)for clinical professionals having less experience. As a result,processing engine 204 may assign higher weights/scores to analyzedfactors associated with clinical professionals having more experience.Based on the evaluation of the various factors, processing engine 204may identify one or more outputs or output categories.

Output engine 206 may be configured to create one or more outputs forthe received information. In aspects, output engine 206 may access theoutputs or output categories identified by processing engine 204. Basedon the identified outputs or output categories, output engine 206 maycreate one or more outputs. As one example, output engine 206 maygenerate a predicted reading time for a mammographic exam in thereceived information based on an analysis by processing engine 204. Thepredicted reading time may represent an amount of reading time, a rangeof reading time (e.g., 15-20 minutes), or a reading time category (e.g.,Low, Medium, High) that an “average” reader requires to read amammographic exam. An “average” reader may be classified as a readerhaving one or more attributes within a specified attribute range.Alternately, the predicted reading time may represent a category orclassification (e.g., fast, medium, or slow) indicating the amount ofreading time that an average reader requires to read a mammographicexam. For instance, a “fast” category may correspond to reading timesunder 10 minutes, a “medium” category may correspond to reading timesbetween 10 minutes and 20 minutes, and a “slow” category may correspondto reading times above 20 minutes.

As another example, output engine 206 may generate a predicted readingtime for a mammographic exam in which that predicted reading timerepresents the amount of reading time, a range of reading time, or areading time category that a specific reader requires to read amammographic exam. The specific user may be a user currently logged intoinput processing system 200, a user who has previously logged into inputprocessing system 200, a user selected by a user currently logged intothe input processing system 200, a user selected by input processingsystem 200, or the like. For example, output engine 206 may generate apredicted reading time that is personalized to a user currently loggedinto input processing system 200. The predicted reading time for theuser may be based on user-profile data and/or previous reading time dataof the user, which may indicate that the user is a comparatively fastreader. As a result, the predicted reading time for the user may belower than the predicted reading time for an average reader or a slowreader.

Alternately, output engine 206 may generate multiple predicted readingtimes. The multiple predicted reading times may correspond to individualreaders or categories of readers. For instance, a first reading time maycorrespond to readers having less than five years of experience, asecond reading time may correspond to readers having between five andten years of experience, and a third reading time may correspond toreaders having more than ten years of experience.

As yet another example, output engine 206 may generate a complexityrating for an image in the received information based on a set offactors or a complexity index. The complexity rating may represent thedifficulty or complexity of reading a mammographic exam or imagesthereof for one or more readers. The difficulty or complexity of readinga mammographic exam may be based on objective and/or subjective factors.Examples of objective factors include, but are not limited to, thenumber and/or type of findings or CAD marks detected in an image, thebreast evaluation location, and breast density and tissue composition.Examples of subjective factors include, but are not limited to, readerexperience, reader knowledge, reader evaluation technique, and readercompetency. In examples, the difficulty or complexity of reading amammographic exam may increase based on the quantity of factorsconsidered. For instance, an analysis of 3-5 factors may be less complexthan an analysis of 10-15 factors. The difficulty or complexity ofreading a mammographic exam may also increase based on the values of thefactors considered. For instance, the complexity of reading amammographic exam may increase as the number CAD marks increases, thenumber CAD marks detected in a specific portion of the breast increases,or the amount of dense breast tissue evaluated increases. The complexityof reading a mammographic exam may also effectively increase forinexperienced readers or less competent readers.

Alternately, output engine 206 may generate multiple complexity ratings.The multiple complexity ratings may correspond to individual readers orcategories of readers. For instance, the complexity rating of“difficult” may be assigned to readers having less than five years ofexperience, and a complexity rating of “moderate” may be assigned toreaders having more than five years of experience.

In aspects, the predicted reading time and/or the complexity value maybe stored and associated with the mammographic exam associated with thepatient. The mammographic exam (or elements thereof) may be provided toone or more destinations, such as a device, an application, or service.The destination(s) may enable a healthcare professional to view andinteract with the mammographic exam (including the predicted readingtime and/or the complexity value) via, for example, the user interfacedescribed above. As a specific example, a reading time and/or thecomplexity value that has been associated with a specific mammographicexam may be displayed to a reader in a patient worklist for the user.Alternately, the reading time and/or the complexity value may bedisplayed to a technologist performing the screening or diagnostic scanson the patient(s). In at least one aspect, output engine 206 may alsocause one or more additional actions to be performed. For instance,based on the predicted reading time and/or the complexity value, outputengine 206 may identify one or more clinical professionals having theexpertise and available time to read the mammographic exam. Reading themammographic exam, as used herein, may refer to various methods andapproaches for reviewing and interpreting images and patientinformation. For example, one reading method may include a readerverifying that images are of satisfactory quality (e.g., no or minimalmotion or blurring). If the images (or at least a portion thereof) areof satisfactory quality, the reader may evaluate the size and symmetryof the breasts. The reader may also compare the current images withpreviously collected images to identify changes between the images. Thecomparison may include viewing different image views of the breasts,such as mediolateral-oblique, mediolateral, cranial-caudal, etc. Theviewings of the image views may be performed in various orders andviewing combinations. The reader may view a 2D image synthesized fromtwo or more tomography images and investigate the CAD marks indicated bythe tomography images. The reader may view various features in theimages, such as calcifications, areas of skin thickening, features thatare associated with types of cancers, speculated masses, etc. The readermay annotate one or more images based on specific areas of interest anddetermine a finding or a result. The finding or result may then berecorded in a report using standardizes methodology or categories, suchas BI-RADS.

In aspects, identify one or more clinical professionals may beaccomplished by output engine 206 by querying one or more HIS devicesfor reader information, such as reading statistics, reader availability,education/expertise, experience, etc. In aspects, a healthcareprofessional receiving the output of output engine 206 may use theoutput to balance or optimize the workloads of available clinicalprofessionals. For example, a clinical professional may prefer readingcomplex mammography exam early in the day, which enables the clinicalprofessional to leave the less complex and/or less time-consumingmammography exam readings for the end of the day. As such, the clinicalprofessional may use the output of output engine 206 to arrange theirworkload accordingly. As another example, a clinical professional mayonly have a small time slot available on a particular day. As such, theclinical professional may use the output of output engine 206 to arrangetheir workload to maximize the number of mammography exam readings thatthe clinical professional may perform in the time slot.

Alternately, the output may be used to automate the balancing oroptimization of the workloads of available clinical professionals. Forinstance, the output of output engine 206 may be provided to a workloadmanagement system/service that is configured to dynamicallycreate/update clinical professional workloads. The workload managementsystem/service may balance the workloads of two clinical professionalssuch that the first clinical professional is assigned ten mammographyexam readings per day, each categorized as having “Fast” reading times,and the second clinical professional is assigned five mammography examreadings per day, each categorized as having “Slow” reading times.Despite the different number of mammography exam readings assigned tothe first and second clinical professional, their respective workloadsmay require approximately the same amount of time to complete.Alternately, the workload management system/service may balance theworkloads of two clinical professionals such that each clinicalprofessional is assigned the same number and mix of complex mammographyexam readings or such that the clinical professional having the mostexperience is assigned a proportionately higher number of complex and/or“Slow” reading time mammography exam readings.

Having described various systems that may be employed by the aspectsdisclosed herein, this disclosure will now describe one or more methodsthat may be performed by various aspects of the disclosure. In aspects,methods 300 and 400 may be executed by an example system, such as system100 of FIG. 1 or input processing system 200 of FIG. 2 . In examples,methods 300 and 400 may be executed on a device comprising at least oneprocessor configured to store and execute operations, programs, orinstructions. However, methods 300 and 400 are not limited to suchexamples. In other examples, methods 300 and 400 may be performed on anapplication or service for automating clinical workflow decisions. In atleast one example, methods 300 and 400 may be executed (e.g.,computer-implemented operations) by one or more components of adistributed network, such as a web service/distributed network service(e.g., cloud service).

FIG. 3 illustrates an example method 300 for predicting reading time ofa mammographic exam as described herein. Example method 300 begins atoperation 302, where a first set of data is collected. In aspects, adata collection component, such as data collection engine 202, maycollect or receive a first set of data from one or more data sources.The first set of data may comprise or relate to 2D and/or 3D breastimage data, image evaluation data, and/or image reader information. Inat least one aspect, the first set of data may comprise labeled and/orunlabeled training data. Examples of breast image data may include, butare not limited to, pixel image data and image header data. Pixel imagedata may be used to derive various attributes of a patient's breast,such as tissue patterns, texture, density, complexity, thickness,volume, and abnormalities. Image header data may provide informationsuch as the type of study (e.g., screening, diagnostic, etc.) performed,the image resolution, the type of hardware system used to collect theimages, the image processing method used, etc. Examples of imageevaluation data may include, but are not limited to, study (e.g.,mammographic exam reading session) open and close times, type of readingtools used (e.g., magnifier, notation tool, measurement tool, etc.),reading tool usage data (as described above with respect to processingengine 204), hanging protocol, workstation hardware/softwareconfiguration, study reading times, number and/or type of studiesperformed, previous patient report data, etc. Examples of image readerinformation may include, but are not limited to, a reader's experience,expertise, certifications, title/classification, workload/status,proficiency rating, reading time/complexity opinions, and age.

At operation 304, the first set of data is provided to a predictivemodel. In aspects, one or more portions of the first set of data may beprovided to an evaluation component, such as processing engine 204. Theevaluation component may be, comprise, or have access to one or morepredictive models. The first set of data may be provided as input to apredictive model to train the predictive model to generate one or moreoutputs. Example outputs include estimated reading times for breastimages, complexity ratings for reading breast images, identification ofrecommended image readers, and time slot availabilities of recommendedimage readers. In at least one aspect, the first set of data may be usedby the evaluation component to generate or update a case complexityindex. For example, the evaluation component may use an index creationmodel or algorithm to generate or update a case complexity index. Thecase complexity index may comprise a range of complexities associatedwith reading one or more images or image types. The case complexityindex may be configured to provide one or more complexity ratings for animage based on a set of factors that may influence the time required toread the image. In examples, generating or updating a case complexityindex may comprise using ML to map a complexity rating to the positiveidentification of one or more factors, factor values, or a range offactor values. For instance, a rating of “easy” may be mapped to a setof features in which breast density is low and the number of lesionsidentified in an image is two or fewer. In other examples, thegenerating or updating a case complexity index may comprise creatingcomplexity rating categories based on scores associated with variousfactors or factor values. For instance, various factors/attributesassociated with a mammographic exam reading may be scored using ascoring model or algorithm. The score for each factor/attribute may beaggregated and used to establish a range of values indicating one ormore rating categories. In some aspects, a trained predictive modeland/or a case complexity index may be provided to, and/or implementedby, one or more systems or devices.

At operation 306, a second set of data is collected. In aspects, a datacollection component, such as data collection engine 202, may collect orreceive a second set of data from one or more data sources. The secondset of data may comprise at least a portion of data that is similar intype, category, and/or value to the first set of data. For example, thesecond set of data may comprise or relate to breast image data, imageevaluation data, and/or image reader information. In some examples,however, the second set of data may not include labeled or unlabeledtraining data.

At operation 308, the second set of data is provided to a trainedpredictive model. In aspects, one or more portions of the second set ofdata may be provided to an evaluation component, such as processingengine 204. The evaluation component may provide the second set of datato a predictive model, such as the predictive model trained duringoperation 304. The trained predictive model may evaluate the second setof data to determine correlations between the second set of data andtraining data used to train the predictive model. For example, based ontraining data used to train a predictive model, the predictive model maydetermine that the reading time of breast images having a particular setof breast attributes (e.g., shape, density, lesions, etc.) varies basedon the attributes of the image reader and the conditions under which themammographic exam reading is performed. For instance, the predictivemodel may identify that images of breasts having an ACR Breast ImagingReporting and Data System (BI-RADS) mammographic density (MD) of Type 1,and 0-2 lesions generally require image readers having 5 or more yearsof experience 10 minutes to read in the morning (when the reader isrelatively rested), and 15 minutes to read in the evening (when thereader is relatively fatigued). The predictive model may furtheridentify that images of breasts having the above attributes (e.g., MDType 1, 0-2 lesions detected) generally require: image readers havingless than 5 years of experience 25 minutes to read in the morning, and30 minutes to read in the evening.

In some aspects, the evaluation component may evaluate the second set ofdata using the case complexity index described above. The evaluation mayinclude identifying, organizing, and/or classifying one or more featuresor attributes of the second set of data. The identifiedfeatures/attributes may be compared to, or evaluated against, the casecomplexity index using decision logic, such as a ML algorithm or a setof evaluation rules. As one example, a predictive model may determinethat predicted reading times for breast images of a particular type orhaving a particular set of attributes are within the 85th percentile forreading time duration (e.g., indicating an increased reading duration ascompared to other breast images of image types). Based on the determinedpercentile rank, the case complexity index may provide an indicationthat the determined/predicted complexity for such images. For instance,the case complexity index may provide a designation of “difficult” forany breast images determined to be within at least 80^(th) percentile.As another example, at least a portion of the data used to determine thepredicted reading time (e.g., breast image data, image evaluation data,and/or image reader information) may be evaluated using the casecomplexity index. The evaluation may include assigning values or scoresto features in the data. A scoring engine or algorithm may be applied tothe assigned values/scores to generate an aggregated mammographic examreading score. For instance, data in the second set of data may befeaturized and used to construct one or more feature vectors. A featurevector, as used herein, may refer to an n-dimensional vector ofnumerical features that represent one or more objects. The featurevector(s) may be applied to, or evaluated against, the case complexityindex. Based on the feature vector values/scores, the case complexityindex may provide a corresponding score or designation indicating thecomplexity or reading a particular breast image or type of breast image.

At operation 310, an estimated mammographic exam reading time isreceived. In aspects, the trained predictive model and/or the casecomplexity index may provide one or more outputs for the second set ofdata. As discussed in operation 304, the outputs may include estimatedmammographic exam reading times, estimated complexity ratings for imagesto be read, recommendations or assignments of image readers, job/taskscheduling dates/time, etc. The output may be provided to one or moreHIS devices and/or healthcare professional devices. Based on the output,a reader and/or a reading session time may be manually or automaticallyassigned for the image(s) in the second set of data. In some aspects,the predictive model output, the case complexity index output, and/orthe statistics and parameters of the resulting mammographic examreading/study, may be provided as input to a predictive model (such asthe predictive model described in operations 304 and 308) and/or acomponent implementing or maintaining the case complexity index. Theinput may be used to further train the predictive model and/or the casecomplexity index. As one example, based on the output of a predictivemodel, an image requiring an estimate reading time of 15 minutes may beassigned to a radiologist having 10 years of experience. Themammographic exam reading/study may actually take the radiologist 25minutes. The estimate reading time, the actual reading time, and theparameters/conditions of the mammographic exam reading/study (e.g.,reading tools used, time of day, radiologist information, etc.) may beprovided to the predictive model. The predictive model may use theinformation to adjust future reading time estimates for the radiologist,or to reevaluate the reading times for images having similar attributesthe image in the mammographic exam reading/study.

FIG. 4 illustrates an example method 400 for predicting readingcomplexity and/or reading time of a mammographic exam as describedherein. Example method 400 begins at operation 402, where a first set ofdata is collected. In aspects, a data collection component, such as datacollection engine 202, may collect or receive a first set of data fromone or more data sources. The first set of data may comprise or relateto 2D and/or 3D breast image data, image evaluation data, and/or imagereader information. In at least one aspect, the first set of data maycomprise labeled and/or unlabeled training data. Examples of breastimage data may include, but are not limited to, pixel image data andimage header data. Pixel image data may be used to derive variousattributes of a patient's breast, such as tissue patterns, density,complexity, thickness, volume, and abnormalities. Image header data mayprovide information such as the type of study (e.g., screening,diagnostic, etc.) performed, the image resolution, the type of hardwaresystem used to collect the images, the image processing method used,etc. Examples of image evaluation data may include, but are not limitedto, study (e.g., mammographic exam reading session) open and closetimes, type of reading tools used (e.g., magnifier, notation tool,measurement tool, etc.), hanging protocol, workstation hardware/softwareconfiguration, study reading times, number and/or type of studiesperformed, previous patient report data, etc. Examples of image readerinformation may include, but are not limited to, a reader's experience,expertise, certifications, title/classification, workload/status,proficiency rating, and age.

At operation 404, a predictive model is trained using the first set ofdata. In aspects, the first set of data may be provided to an evaluationcomponent, such as processing engine 204. The evaluation component maybe, comprise, or have access to one or more predictive models. The firstset of data may be provided as input to a predictive model to train thepredictive model to generate one or more outputs. Example outputsinclude estimated complexity ratings or a complexity system/componentfor reading or interpreting mammographic exam data. As a particularexample, the first set of data may comprise a labeled or annotatedbreast image, a reported amount of time for reading the image, areported or suggested complexity rating for reading the image, andprofile information for the reader of the image. The first set of datamay be provided to a predictive model. The predictive model may use oneor more data correlation techniques to determine correlations betweenthe reported/suggested complexity rating and the other factors/datapoints in the first set of data.

At operation 406, a case complexity index may be estimated. In aspects,a predictive model may use the first set of data to estimate or update acase complexity index. A case complexity index, as used herein, mayrefer to a data structure or component comprising mappings of complexityvalues or labels to data/factors that may influence the amount of timerequired to read or interpret a mammographic exam. Estimating orupdating the case complexity index may comprise using an index creationalgorithm, a data mapping utility, or data correlation algorithm. Thecase complexity index may be configured to provide one or morecomplexity ratings or labels for collected mammographic exam data basedon a set of factors or data points associated with the collectedmammographic exam data. In examples, the set of factors or data pointsmay influence the time required to read the collected breast image. Forinstance, a rating of “2” on a scale of 1 to 5 (where “1” is very easy,“2” is easy, “3” is moderate, “4” is difficult, and “5” is verydifficult) may be mapped to a set of features in which at least two ofthe following factors is satisfied: breast density is low, the number oflesions identified in an image is one or fewer, and a reader has morethan 10 years of experience.

At operation 408, the case complexity index may be output. In aspects,an estimated/updated case complexity index may be output by a predictivemodel or the evaluation component. The case complexity index that isoutput may be a standalone executable file or utility. Alternatively,the output case complexity index may be integrated into a service,application, or system. As one example, the case complexity index (or aninstance thereof) may be distributed to and integrated into a userinterface of one or more workstations (e.g., image acquisitionworkstations, image review workstations, other HIS computing devices,etc.). The user interface many enable a healthcare professional toevaluate and/or modify the mappings, mapping logic, classifications,and/or category values of the case complexity index. The user interfacemay additionally enable a healthcare professional to assign a weightedvalue or importance to various factors evaluated by the case complexityindex. For instance, a healthcare professional that is more interestedin the number of lesions identified in a breast image than breastdensity, may assign a higher importance to the number of identifiedlesions. Assigning a higher importance may include applying a multiplier(such as ×1.25) to factors relating to the number of identified lesions,or setting a designation for a particular value range (e.g., mapping “0”lesions to “very easy,” “1-2” lesions to “easy,” etc.).

At operation 410, a second set of data is collected. In aspects, a datacollection component, such as data collection engine 202, may collect orreceive a second set of data from one or more data sources. The secondset of data may comprise at least a portion of data that is similar intype, category, and/or value to the first set of data. For example, thesecond set of data may comprise or relate to breast image data, imageevaluation data, and/or image reader information. In some examples,however, the second set of data may not include labeled or unlabeledtraining data.

At operation 412, a predictive model is trained using the second set ofdata. In aspects, the second set of data and/or data relating to thecase complexity index may be provided to an evaluation component, suchas processing engine 204. The evaluation component may use the providedinformation to train a predictive model. In some examples, thepredictive model may be the predictive model trained at operation 404.In other examples, a new or an alternate predictive model may betrained. The predictive model may be trained to generate one or moreoutputs. Example outputs include estimated mammographic exam readingtimes, estimated complexity ratings for images to be read,recommendations or assignments of image readers, job/task schedulingdates/time, etc. For instance, the predictive model may determine thatthe reading time of breast images having a particular set of breastattributes (e.g., shape, density, lesions, etc.) varies based on theattributes of the image reader and the conditions under which themammographic exam reading is performed. Based on the determination, thepredictive model may use one or more data correlation techniques todetermine correlations between known reading times for images and thefactors/data points corresponding to the known reading times.

At operation 414, a reading time may be estimated. In aspects, thetrained predictive model may use the second set of data to generate oneor more outputs. For example, the predictive model may evaluate thesecond set of data to determine correlations between the data used totrain the predictive model and the second set of data. Based on thedetermined correlations, one or more reading times for a breast imageassociated with the second set of data may be estimated. For instance,based on identifying that an imaged breast has a BI-RADS mammographicdensity of Type 2 and no lesions have been identified in the breastimage, the predictive model may estimate an mammographic exam readingtime of 20 minutes for image readers having less than five years ofexperience, and an mammographic exam reading time of 10 minutes forimage readers having five or more years of experience. In anotherexample, based on the correlations determined by the predictive model, acomplexity rating/system may alternately or additionally beestimated/generated. For instance, based on a BI-RADS mammographicdensity classification, a lesion count, and/or an estimated readingtime, the predictive model may estimate an reading complexity for amammographic exam. Alternately, the predictive model may use the casecomplexity index generated or updated at operation 406 to estimate areading complexity. For instance, the predictive model may provide atleast a portion of second set of data to the case complexity index. Inresponse, the case complexity index may provide a complexity rating forreading a mammographic exam to the predictive model.

At operation 416, an estimated reading time may be output. In aspects,one or more estimated mammographic exam reading times may be output bythe predictive model. The estimated reading time(s) may correspond to anindividual image reader, multiple image readers, or one or morecategories of readers. For example, an estimated reading time mayrepresent the amount of time an “average” image reader requires to reada mammographic exam. Alternately, each estimated reading time mayrepresent the amount of time that an “average” image reader in aparticular category of image readers requires to read a mammographicexam. In some aspects, the predicted reading time may be provided to ahealthcare professional via, for example, the user interface describedabove. Providing the predicted reading time may cause one or moreadditional actions to be performed. For instance, based on the predictedreading time, one or more radiologists having the expertise andavailable time to read a mammographic exam may be identified and/ornotified. The identification/notification may be accomplished byquerying one or more HIS devices for radiologist information, such asreading statistics, availability, education/expertise, experience, etc.

FIG. 5A illustrates an example user interface 500 that is associatedwith the automated clinical workflow decisions described herein. Inexamples, user interface 500 represents software a technologist uses ona mammography acquisition workstation. The software may be used tocollect images during a breast screening exam from an X-ray imagingsystem, such as X-ray Imaging system 204, and/or to review collectedimages during a breast screening exam. User interface 500 comprisesbutton 502, which activates an “Analytics” dialog when selected.

FIG. 5B illustrates Analytics dialog 510, which is displayed when button502 of FIG. 5A is selected. Analytics dialog 510 comprises button 512,analysis result section 514, and reading time indicator 516 and readingcomplexity indicator 518. In aspects, when button 512 is selected, imageevaluation software is launched, and one or more collected images areanalyzed using the techniques described in FIG. 3 and FIG. 4 . As aresult of the analysis, analysis result section 514 is at leastpartially populated with data, such as reading time indicator 516 andreading complexity indicator 518. In FIG. 5B, reading time indicator 516indicates that the reading time for the analyzed mammographic exam is“Medium” and reading complexity indicator 518 indicates that the readingcomplexity for the analyzed mammographic exam is “High.” The “Medium”reading time may indicate that an average (or a specific) reader mayrequire a medium or an average amount of time to read the mammographicexam. The “High” complexity may indicate that is difficult to accuratelyinterpret and/or identify one or more aspects of the mammographic exam.

FIG. 6 illustrates an exemplary suitable operating environment fordetecting X-ray tube output roll off described in FIG. 1 . In its mostbasic configuration, operating environment 600 typically includes atleast one processing unit 602 and memory 604. Depending on the exactconfiguration and type of computing device, memory 604 (storing,instructions to perform the X-ray tube roll off detection techniquesdisclosed herein) may be volatile (such as RAM), non-volatile (such asROM, flash memory, etc.), or some combination of the two. This mostbasic configuration is illustrated in FIG. 6 by dashed line 606.Further, environment 600 may also include storage devices (removable,608, and/or non-removable, 610) including, but not limited to, magneticor optical disks or tape. Similarly, environment 600 may also have inputdevice(s) 614 such as keyboard, mouse, pen, voice input, etc. and/oroutput device(s) 616 such as a display, speakers, printer, etc. Alsoincluded in the environment may be one or more communication connections612, such as LAN, WAN, point to point, etc. In embodiments, theconnections may be operable to facility point-to-point communications,connection-oriented communications, connectionless communications, etc.

Operating environment 600 typically includes at least some form ofcomputer readable media. Computer readable media can be any availablemedia that can be accessed by processing unit 602 or other devicescomprising the operating environment. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media includes volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information such as computer readableinstructions, data structures, program modules or other data. Computerstorage media includes, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disks (DVD) or other opticalstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or any other non-transitory medium whichcan be used to store the desired information. Computer storage mediadoes not include communication media.

Communication media embodies computer readable instructions, datastructures, program modules, or other data in a modulated data signalsuch as a carrier wave or other transport mechanism and includes anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared, microwave, and other wireless media.Combinations of the any of the above should also be included within thescope of computer readable media.

The operating environment 600 may be a single computer operating in anetworked environment using logical connections to one or more remotecomputers. The remote computer may be a personal computer, a server, arouter, a network PC, a peer device or other common network node, andtypically includes many or all of the elements described above as wellas others not so mentioned. The logical connections may include anymethod supported by available communications media. Such networkingenvironments are commonplace in offices, enterprise-wide computernetworks, intranets and the Internet.

The embodiments described herein may be employed using software,hardware, or a combination of software and hardware to implement andperform the systems and methods disclosed herein. Although specificdevices have been recited throughout the disclosure as performingspecific functions, one of skill in the art will appreciate that thesedevices are provided for illustrative purposes, and other devices may beemployed to perform the functionality disclosed herein without departingfrom the scope of the disclosure.

This disclosure describes some embodiments of the present technologywith reference to the accompanying drawings, in which only some of thepossible embodiments were shown. Other aspects may, however, be embodiedin many different forms and should not be construed as limited to theembodiments set forth herein. Rather, these embodiments were provided sothat this disclosure was thorough and complete and fully conveyed thescope of the possible embodiments to those skilled in the art.

Although specific embodiments are described herein, the scope of thetechnology is not limited to those specific embodiments. One skilled inthe art will recognize other embodiments or improvements that are withinthe scope and spirit of the present technology. Therefore, the specificstructure, acts, or media are disclosed only as illustrativeembodiments. The scope of the technology is defined by the followingclaims and any equivalents therein.

1-20. (canceled)
 21. A method of analyzing medical image data, themethod comprising: receiving, from an X-ray imaging system, mammographicexam data for a patient, wherein the mammographic exam data includes:breast image data including one or more X-ray images of the patient'sbreast tissue; and one or more image factors determined according toprocessing of the breast image data; providing the mammographic examdata to a predictive model; determining, by the predictive model,correlations between the one or more image factors and training factors,wherein the training factors are determined based on mammographic examdata for one or more patients and evaluation data for one or more examreaders; determining an expected reading time for the mammographic examdata based on the correlations; assigning a complexity label to thebreast image data based on at least one of the expected reading time,the correlations, and a portion of the breast image data; receiving aselection of the mammographic exam data for the patient; and in responseto receiving the selection of the mammographic exam data, displaying thecomplexity label in association with the mammographic exam data.
 22. Themethod of claim 21, wherein the complexity label is assigned accordingto a complexity index; and wherein the complexity index is determinedbased on the mammographic exam data for one or more patients andevaluation data for one or more exam readers.
 23. The method of claim22, wherein the complexity index comprises mappings between complexitylabels and factors affecting an amount of time required to interpretmammographic exam data, wherein the factors include image factorsderived from the mammographic exam data and reader factors derived fromevaluation data.
 24. The method of claim 21, further comprisingdetermining a reading priority based on the correlations.
 25. The methodof claim 24, further comprising displaying the reading priority inresponse to the selection of the mammographic exam data.
 26. The methodof claim 21, further comprising adjusting the expected reading timebased on the complexity label assigned.
 27. The method of claim 21,further comprising configuring a first workload for a first clinicianand a second workload for a second clinician, wherein a first combinedreading time of the first workload and a second combined reading time ofthe second workload differ by less than a threshold amount of time. 28.The method of claim 27, wherein the first workload comprises a firstnumber of exam readings and the second workload comprises a secondnumber of exam readings, and the first number of exam readings differsfrom the second number of exam readings.
 29. The method of claim 21,wherein determining, by the predictive model, the correlations betweenthe one or more image factors and the training factors, includes scoringeach of the one or more image factors based on a scoring model.
 30. Themethod of claim 29, wherein the complexity label is associated with arange of scores.
 31. The method of claim 30, wherein assigning thecomplexity label to the breast image data includes determining a totalscore for the one or more image factors falls within the range ofscores.
 32. The method of claim 30, wherein assigning the complexitylabel to the breast image data based the expected reading time comprisesdetermining the expected reading time falls within a predeterminedrange, wherein the predetermined range comprises a percentile ofaggregated reading time durations.
 33. The method of claim 21, whereinthe one or more image factors include one or more of a number of regionsof interest in the breast image data, a type of region of interest inthe breast image data, a number of computer aided design (CAD) marks inthe breast image data, a type CAD marks in the breast image data, abreast evaluation location, a breast density, and a tissue composition.34. The method of claim 33, wherein processing of the breast image dataincludes one or more of artifact reduction and de-noising.
 35. Themethod of claim 21, further comprising receiving one or more readerfactors, wherein determining the expected reading time for themammographic exam data is further based on the one or more readerfactors.
 36. The method of claim 35, wherein the one or more readerfactors include factors characterizing an average reader.
 37. The methodof claim 35, wherein the one or more reader factors and the selection ofthe mammographic exam data are each associated with a same reader. 38.The method of claim 35, wherein the one or more reader factors includesone or more of a reader experience score, a reader knowledge score, areader evaluation technique score, and a reader competency score. 39.The method of claim 21, wherein the image factors includes a quantity offactors and the complexity label is associated with a threshold numberof factors, and wherein associating the complexity label with themammographic exam data comprises determining the quantity of factorsexceeds the threshold number of factors; and wherein the quantity offactors includes one or more of a number of computer aided design (CAD)marks in the breast image data, a number of CAD marks in a specificportion of the breast image data, or an amount of dense tissue in thebreast image data.
 40. A system for analyzing medical image data anddetermining a complexity of the medical image data, the systemcomprising: a processor; and memory coupled to the processor, the memorycomprising computer executable instructions that, when executed by theprocessor, performs a method comprising: receiving, from an X-rayimaging system, mammographic exam data for a patient, wherein themammographic exam data includes: breast image data including one or moreX-ray image of the patient's breast tissue; and one or more imagefactors determined according to processing of the breast image data;providing the mammographic exam data to a predictive model; determining,by the predictive model, correlations between the one or more imagefactors and a training factors, wherein the training factors aredetermined based on mammographic exam data for one or more patients andevaluation data for one or more exam readers; determining an expectedreading time for the mammographic exam data based on the correlations;assigning a complexity label to the breast image data based on at leastone of the expected reading time, the correlations, and a portion of thebreast image data; receiving a selection of the mammographic exam datafor the patient; and in response to receiving the selection of themammographic exam data, displaying the complexity label in associationwith the mammographic exam data.